Smartphone GPS signatures of patients undergoing spine surgery correlate with mobility and current gold standard outcome measures

Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery

BACKGROUND AND OBJECTIVES

Neurosurgeons and hospitals devote tremendous resources to improving recovery from lumbar spine surgery. Current efforts to predict surgical recovery rely on one-time patient report and health record information. However, longitudinal mobile health (mHealth) assessments integrating symptom dynamics from ecological momentary assessment (EMA) and wearable biometric data may capture important influences on recovery. Our objective was to evaluate whether a preoperative mHealth assessment integrating EMA with Fitbit monitoring improved predictions of spine surgery recovery.

METHODS

Patients age 21-85 years undergoing lumbar surgery for degenerative disease between 2021 and 2023 were recruited. For up to 3 weeks preoperatively, participants completed EMAs up to 5 times daily asking about momentary pain, disability, depression, and catastrophizing. At the same time, they were passively monitored using Fitbit trackers. Study outcomes were good/excellent recovery on the Quality of Recovery-15 (QOR-15) and a clinically important change in Patient-Reported Outcomes Measurement Information System Pain Interference 1 month postoperatively. After feature engineering, several machine learning prediction models were tested. Prediction performance was measured using the c-statistic.

RESULTS

A total of 133 participants were included, with a median (IQR) age of 62 (53, 68) years, and 56% were female. The median (IQR) number of preoperative EMAs completed was 78 (61, 95), and the median (IQR) number of days with usable Fitbit data was 17 (12, 21). 63 patients (48%) achieved a clinically meaningful improvement in Patient-Reported Outcomes Measurement Information System pain interference. Compared with traditional evaluations alone, mHealth evaluations led to a 34% improvement in predictions for pain interference (c = 0.82 vs c = 0.61). 49 patients (40%) had a good or excellent recovery based on the QOR-15. Including preoperative mHealth data led to a 30% improvement in predictions of QOR-15 (c = 0.70 vs c = 0.54).

CONCLUSION

Multimodal mHealth evaluations improve predictions of lumbar surgery outcomes. These methods may be useful for informing patient selection and perioperative recovery strategies.

Machine learning allows expert level classification of intraoperative motor evoked potentials during neurosurgical procedures

OBJECTIVE

To develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts.

BACKGROUND

There is an unseized opportunity to apply ML analytic techniques to the world of intraoperative neuromonitoring (IOM). MEPs are the ideal candidates given the importance of their correct interpretation during a surgical operation to the brain or the spine. In this work, we develop and test a set of different ML models for muscle identification using intraoperative MEPs and compare their performance to human experts. In addition, we provide a review of the available literature on current ML applications to IOM data in neurosurgery.

METHODS

We trained and tested five different ML classifiers on a MEP database developed from six different muscles in patients who underwent brain or spinal cord surgery. MEPs were obtained by both transcranial (TES) and direct cortical stimulation (DCS) protocols. The models were evaluated within a single patient and on previously unseen patients, considering signals from TES and DCS both independently and mixed. Ten expert neurophysiologists classified a set of 50 randomly selected MEPs, and their performance was compared to the best performing model.

RESULTS

A total of 25.423 MEPs were included in the study. Random Forest proved to be the best performing model with 99 % accuracy in the single patient dataset task and a 78 %-94 % accuracy range on previously unseen patients. The model performance was maximized by representing MEPs as a set of features typically employed in signal processing compared to traditional neurophysiological parameters. The classification ability of the Random Forest model between six different muscles and across different MEP acquisition modalities (79 %) significantly exceeded that of human experts (mean 48 %).

CONCLUSIONS

Carefully selected ML models proved to have reliable capacity of extracting meaningful information to classify intraoperative MEPs using a limited number of features, proving robustness across patients and signal acquisition modalities, outperforming human experts, and with the potential to act as decision support systems to the IOM team. Such encouraging results lay the path to further explore the underlying nature of clinically important signals, with the aim to continue to produce useful applications to make surgeries safer and more efficient.

Beyond the physical: Digital phenotyping and the complexity of surgical recovery

Digital phenotyping, the moment-by-moment quantification of human behavior in situ using data from personal digital devices, is a potentially powerful tool for increasing understanding of recovery from surgery. While physical metrics are often emphasized, measures of emotional, cognitive, and psychosocial function are important aspects for the surgeon, a better understanding of which can lead to improved preoperative counseling and optimization, shared decision-making, and monitoring of recovery after surgery. A growing number of studies have begun to characterize these techniques. Ultimately, this tool may provide rich data about the perioperative period that will help surgeons and patients alike.

Digital Phenotyping, Wearables, and Outcomes

There is a significant need for robust and objective outcome assessments in spine surgery. Constant monitoring via smartphones and wearable devices has the potential to fill this role by providing an in-depth picture of human well-being, creating an unprecedented amount of objective data to augment clinical decision-making. The metrics obtained from continuous patient monitoring increase the amount and ecological validity of data relevant to spine surgery. This can provide physicians with patient and disease-specific medical information, facilitating personalized patient care.

The Role of Mobile Health Technology in Perioperative Spinal Care: A Systematic Scoping Review and Narrative Synthesis

Smartphone applications play a crucial role in contemporary healthcare by aiming to enhance patient care through technology. Mobile health (mHealth) applications have proven to have transformative potential in enhancing patients' outcomes in candidates undergoing orthopedic and spinal surgery. In the context of the pervasive use of smartphones and the exponential growth of mHealth apps, totaling over 99,000 in 2021, the applications had a significant impact on lifestyle management, supporting initiatives like smoking cessation with motivational reminders and progress tracking. Patient compliance is significantly enhanced, reducing surgery cancellations and improving outcomes through effective adherence to pre-operative treatments and instructions. Physiotherapy receives a substantial boost as mHealth facilitates video-guided exercises, potentially improving compliance and treatment outcomes. Data collection takes on innovative dimensions, with mHealth apps capturing post-operative metrics like physical activity, offering valuable insights into patient recovery trends. Remote care is streamlined through features like photo uploads and direct messaging, proving especially beneficial in times of crises such as the COVID-19 pandemic. Despite these merits, challenges emerge, including issues related to technological literacy, potential discrimination due to paywalls, and concerns about patient data confidentiality. Overcoming these challenges requires standardized approaches, legislative measures, and ongoing research to refine and optimize mHealth applications for diverse healthcare settings.

Smartphone-based activity tracking for spine patients: Current technology and future opportunities

Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients. Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology. Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors. There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.

State-of-the-art Applications of Patient-Reported Outcome Measures in Spinal Care

Patient-reported outcome measures (PROMs) assign objective measures to patient's subjective experiences of health, pain, disability, function, and quality of life. PROMs can be useful for providers in shared decision making, outcome assessment, and indicating patients for surgery. In this article, we provide an overview of the legacy PROMs used in spinal care, recent advancements in patient-reported outcomes, and future directions in PROMs. Recent advances in patient-reported outcome assessments have included standardization of measurement tools, integration of data collection into workflow, and applications of outcome measures in predictive models and decision-making tools. Continual appraisal of instruments and incorporation into artificial intelligence and machine learning analytics will continue to augment the delivery of high-value spinal care.

Digital Biomarkers and the Evolution of Spine Care Outcomes Measures: Smartphones and Wearables

Over the past generation, outcome measures in spine care have evolved from a reliance on clinician-reported assessment toward recognizing the importance of the patient's perspective and the wide incorporation of patient-reported outcomes (PROs). While patient-reported outcomes are now considered an integral component of outcomes assessments, they cannot wholly capture the state of a patient's functionality. There is a clear need for quantitative and objective patient-centered outcome measures. The pervasiveness of smartphones and wearable devices in modern society, which passively collect data related to health, has ushered in a new era of spine care outcome measurement. The patterns emerging from these data, so-called "digital biomarkers," can accurately describe characteristics of a patient's health, disease, or recovery state. Broadly, the spine care community has thus far concentrated on digital biomarkers related to mobility, although the researcher's toolkit is anticipated to expand in concert with advancements in technology. In this review of the nascent literature, we describe the evolution of spine care outcome measurements, outline how digital biomarkers can supplement current clinician-driven and patient-driven measures, appraise the present and future of the field in the modern era, as well as discuss present limitations and areas for further study, with a focus on smartphones (see Supplemental Digital Content , http://links.lww.com/NEU/D809 , for a similar appraisal of wearable devices).

Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease

BACKGROUND

Pain evaluation remains largely subjective in neurosurgical practice, but machine learning provides the potential for objective pain assessment tools.

OBJECTIVE

To predict daily pain levels using speech recordings from personal smartphones of a cohort of patients with diagnosed neurological spine disease.

METHODS

Patients with spine disease were enrolled through a general neurosurgical clinic with approval from the institutional ethics committee. At-home pain surveys and speech recordings were administered at regular intervals through the Beiwe smartphone application. Praat audio features were extracted from the speech recordings to be used as input to a K-nearest neighbors (KNN) machine learning model. The pain scores were transformed from a 0 to 10 scale to low and high pain for better discriminative capacity.

RESULTS

A total of 60 patients were enrolled, and 384 observations were used to train and test the prediction model. Using the KNN prediction model, an accuracy of 71% with a positive predictive value of 0.71 was achieved in classifying pain intensity into high and low. The model showed 0.71 precision for high pain and 0.70 precision for low pain. Recall of high pain was 0.74, and recall of low pain was 0.67. The overall F1 score was 0.73.

CONCLUSION

Our study uses a KNN to model the relationship between speech features and pain levels collected from personal smartphones of patients with spine disease. The proposed model is a stepping stone for the development of objective pain assessment in neurosurgery clinical practice.

Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery

Personalized medicine has made a tremendous impact on patient care. Although initially, it revolutionized pharmaceutical development and targeted therapies in oncology, it has also made an important impact in orthopaedic surgery. The field of spine surgery highlights the effect of personalized medicine because the improved understanding of spinal pathologies and technological innovations has made personalized medicine a key component of patient care. There is evidence for several of these advancements to support their usage in improving patient care. Proper understanding of normative spinal alignment and surgical planning software has enabled surgeons to predict postoperative alignment accurately. Furthermore, 3D printing technologies have demonstrated the ability to improve pedicle screw placement accuracy compared with free-hand techniques. Patient-specific, precontoured rods have shown improved biomechanical properties, which reduces the risk of postoperative rod fractures. Moreover, approaches such as multidisciplinary evaluations tailored to specific patient needs have demonstrated the ability to decrease complications. Personalized medicine has shown the ability to improve care in all phases of surgical management, and several of these approaches are now readily available to orthopaedic surgeons.

The Environmental Impact of Spine Surgery and the Path to Sustainability

STUDY DESIGN

Narrative literature review.

OBJECTIVE

The aim of this study was to review published literature discussing sustainable health care and to identify aspects that pertain to spine surgery.

SUMMARY OF BACKGROUND DATA

In recent years, research has investigated the contribution of surgical specialties to climate change. To our knowledge, no article has yet been published discussing the impact specific to spinal procedures and possible mitigation strategies.

METHODS

A literature search was performed for the present study on relevant terms across four electronic databases. References of included studies were also investigated.

RESULTS

Spine surgery has a growing environmental impact. Investigations of analogous specialties find that procurement is the single largest source of emissions. Carbon-conscious procurement strategies will be needed to mitigate this fully, but clinicians can best reduce their impact by adopting a minimalist approach when using surgical items. Reduced wastage of disposable goods and increased recycling are beneficial. Technology can aid remote access to clinicians, and also enable patient education.

CONCLUSIONS

Spine-surgery-specific research is warranted to evaluate its carbon footprint. A broad range of measures is recommended from preventative medicine to preoperative, intraoperative, and postoperative spine care.

LEVEL OF EVIDENCE

5.

Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates

BACKGROUND

Rapid growth in smartphone use has expanded opportunities to use mobile health (mHealth) technology to collect real-time patient-reported and objective biometric data. These data may have important implication for personalized treatments of degenerative spine disease. However, no large-scale study has examined the feasibility and acceptability of these methods in spine surgery patients.

OBJECTIVE

To evaluate the feasibility and acceptability of a multimodal preoperative mHealth assessment in patients with degenerative spine disease.

METHODS

Adults undergoing elective spine surgery were provided with Fitbit trackers and sent preoperative ecological momentary assessments (EMAs) assessing pain, disability, mood, and catastrophizing 5 times daily for 3 weeks. Objective adherence rates and a subjective acceptability survey were used to evaluate feasibility of these methods.

RESULTS

The 77 included participants completed an average of 82 EMAs each, with an average completion rate of 86%. Younger age and chronic pulmonary disease were significantly associated with lower EMA adherence. Seventy-two (93%) participants completed Fitbit monitoring and wore the Fitbits for an average of 247 hours each. On average, participants wore the Fitbits for at least 12 hours per day for 15 days. Only worse mood scores were independently associated with lower Fitbit adherence. Most participants endorsed positive experiences with the study protocol, including 91% who said they would be willing to complete EMAs to improve their preoperative surgical guidance.

CONCLUSION

Spine fusion candidates successfully completed a preoperative multimodal mHealth assessment with high acceptability. The intensive longitudinal data collected may provide new insights that improve patient selection and treatment guidance.